Video deblurring.  Early works on video deblurring mostly attempt to restore images by estimating blur kernels [1]}, [2]}, [3]}, [4]}.
Recently, Su  [5]} align neighboring frames using homography and optical flows and use a CNN to restore frames.
Kim  [6]} employ a recurrent neural network architecture and propose a dynamic temporal blending technique to enforce temporal consistency among frames.
Instead of explicit alignment, Zhou  [7]} use dynamic filters to adaptively align frame features. However, their method is restricted by the limited local receptive field and is less effective for fast motions.
The works [8]}, [9]} estimate sharpness as a prior and utilize sharp images as exemplars to aid the learning of deblurring networks.
Zhang  [10]} use adversarial loss to improve visual quality of estimated sharp images.
However, their discriminator only considers a single output frame, thus neglecting temporal constraints.
Our training paradigm encourages temporal consistency among multiple output frames, thus regularizing the model to produce temporally-consistent deblurred results.
